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    New Machine Learning Research from Remote Sensing Technology Institute Discussed (SAR Coherence Estimation by Composition of Subsample Estimates and Machine Lea rning)

    85-85页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Oberpfaffenhofen, Germany, by NewsRx correspondents, research stated, "Synthetic aperture radar ( SAR) coherence magnitude is an essential parameter in SAR interferometry." Our news reporters obtained a quote from the research from Remote Sensing Techno logy Institute: "This is the reason why current interferometric wide area ground motion services require the estimation of the coherence magnitude as accurately and computationAlly effectively as possible. The objective of this article is t o improve the accuracy of this coherence estimation compared to known estimators , especiAlly when estimating low coherences and working with a smAll, i.e., N $ <$ 30, but also large number of samples, i.e ., hundred or more. Precisely, this article proposes the interferometric coheren ce magnitude estimation by composition of subsample estimates and machine learni ng (ML). The principle is to partition the given sample and to estimate coherenc es on these independent subsamples using different coherence magnitude estimator s. It results in a nonparametric and automated statistical inference. It is show n that the composite ML estimator has a high estimation quality yet without prio r information, provides a deterministic estimate and is numericAlly efficient, i t is suitable for general interferometric synthetic aperture radar applications and operational systems."
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    New Artificial Intelligence Study Findings Recently Were Reported by Researchers at Texas A&M University (Explainable Artificial Intelligence Predi ction of Defect Characterization In Composite Materials)

    86-87页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Colle ge Station, Texas, by NewsRx correspondents, research stated, "Nondestructive e valuation (NDE) techniques are integral across diverse applications for void det ection within composites. Infrared (IR) thermography (IRT) is a prevalent NDE te chnique that utilizes reverse heat transfer principles to infer defect character istics by analyzing temperature distribution." Financial support for this research came from Department of Aerospace Engineerin g at Texas A M University. Our news editors obtained a quote from the research from Texas A&M University, "Although the forward heat transfer problem is well-posed, its inver se counterpart lacks uniqueness, posing non-unique solutions. The present study performs simulations using finite element analysis (FEA) in defective (a penny-s haped defect) composites through which the heat transfer flux is modeled. A tota l of 2100 simulations with various defect positions and sizes (depth, size, and thickness) are executed, and the corresponding surface temperature vs. time and vs. distance diagrams are extracted. The FEA outputs provide ample input data fo r developing an explainable artificial intelligence (XAI) model to estimate the defect characteristics. A detailed feature engineering task is performed to sele ct the representative information from the diagrams. Explainable decision tree-b ased machine learning (ML) models with transparent decision paths based on deriv ed features are developed to predict the defect depth, size, and thickness. The ML models' results suggest superb accuracy (R2 R 2 = 0.92 to 0.99) across All th ree defect characteristics."
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    Fujian Medical University Union Hospital Reports Findings in Thyroidectomy (Deta iled analysis of learning phases and outcomes in robotic and endoscopic thyroide ctomy)

    87-88页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery - Thyroidectom y is the subject of a report. According to news reporting out of Fuzhou, People' s Republic of China, by NewsRx editors, research stated, "Thyroid surgery has un dergone significant transformation with the introduction of minimAlly invasive t echniques, particularly robotic and endoscopic thyroidectomy. These advancements offer improved precision and faster recovery but also present unique chAllenges ." Our news journalists obtained a quote from the research from Fujian Medical Univ ersity Union Hospital, "This study aims to compare the learning curves, operatio nal efficiencies, and patient outcomes of robotic versus endoscopic thyroidectom y. A retrospective cohort study was conducted, analyzing 258 robotic (da Vinci) and 214 endoscopic thyroidectomy cases. Key metrics such as operation duration, drainage volume, lymph node dissection outcomes, and hypoparathyroidism incidenc e were assessed to understand surgical learning curves and efficiency. Robotic t hyroidectomy showed a longer learning curve with initiAlly longer operation time s and higher drainage volumes but superior lymph node dissection outcomes. Both techniques were safe, with no permanent hypoparathyroidism or recurrent laryngea l nerve damage reported. The study delineated four distinct stages in the roboti c and endoscopic surgery learning curve, each marked by specific improvements in proficiency. Endoscopic thyroidectomy displayed a shorter learning curve, leadi ng to quicker operational efficiency gains. Robotic and endoscopic thyroidectomi es are viable minimAlly invasive approaches, each with its learning curves and e fficiency metrics. Despite initial chAllenges and a longer learning period for r obotic surgery, its benefits in complex dissections may justify specialized trai ning. Structured training programs tailored to each technique are crucial for im proving outcomes and efficiency."
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    Studies from University of Brescia Have Provided New Information about Robotics (Full Pose Measurement System for Industrial Robots Kinematic Calibration Based On a Sensorized Spatial Linkage Mechanism)

    88-89页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news reporting out of Brescia, Italy, by NewsRx edi tors, research stated, "This paper presents a low-cost pose measuring device cap able of simultaneously measuring All six coordinates (3 translations and 3 rotat ions) of a rigid body with respect to a given reference frame. The proposed syst em consists of a mechanical chain of rigid bodies and two encoders." Our news journalists obtained a quote from the research from the University of B rescia, "The mechanism is a spatial four-bar linkage system with a symmetrical R evolute-Spherical-SphericalRevolute (RSSR) kinematic structure, where two encode rs measure the rotation of the revolute joints. The mechanism is investigated th eoreticAlly and solved kinematicAlly using a numerical estimation method. The un certainty of the pose determination, caused by the repeatability of the sensors, is estimated, as well as the achievable measurement range. A low uncertainty is achieved by a suitable design of the proposed kinematic chain. The mechanism is easy to realize with low tolerances and the correct definition of the length of the links Allows a quite large workspace. The system can be profitably used in the calibration of robots or multi-axis machine tools where the actual pose of the gripper or spindle must be measured over the workspace of the machine."
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    New Findings on Machine Learning from SRM Institute of Science and Technology Su mmarized (Optimized Tiny Machine Learning and Explainable Ai for Trustable and E nergy-efficient Fog-enabled Healthcare Decision Support System)

    89-90页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news originating from Tamil Nadu, India, by NewsR x correspondents, research stated, "The Internet of things (IoT)-based healthcar e decision support system plays a crucial role in modern medicine, especiAlly wi th the rise in chronic illnesses and an aging population necessitating continuou s remote health monitoring. Current healthcare decision support systems struggle to deliver timely and accurate decisions with minimal latency due to limited re al-time healthcare data and inefficient computational resources." Our news journalists obtained a quote from the research from the SRM Institute o f Science and Technology, "There is a critical need for an optimized, energy-eff icient machine learning model that reliably supports remote health monitoring wi thin IoT and fog computing environments. Our study proposes an Optimized Tiny Ma chine Learning (TinyML) and Explainable AI (XAI) binary classification model for a trustable and energy-efficient healthcare decision support system, leveraging fog computing to optimize performance. The fog-based approach improves response times and enhances bandwidth usage, addressing critical needs such as reduced l atency, higher bandwidth utilization, and decreased packet loss. To further impr ove efficiency, we incorporate the innovative mLZW data compression technique, s ignificantly enhancing data communication efficiency and reducing response time to critical health alerts. However, limited real-time healthcare data records ch Allenge machine learning classification performance. By implementing a TinyML al gorithm, our system demonstrates superior performance to other machine learning models. The proposed optimized TinyML model achieves an impressive F1 score of 0 .93 for health abnormalities detection, emphasizing its robustness and effective ness."
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    Tianjin University Details Findings in Support Vector Machines (Broadcasting Map Construction Method Based On Particle Swarm Optimization-assisted Support Vecto r Machine Integrated Model)

    90-91页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Support Vector Machin es have been presented. According to news reporting out of Tianjin, People's Rep ublic of China, by NewsRx editors, research stated, "The rapid development of te rrestrial broadcasting services has heightened the demands for terrestrial broad casting coverage capabilities and system planning. This article proposed an inte grated prediction method suitable for multiband and multiscene applications to c onstruct high-quality radio maps in frequency modulation (FM) bands, defined as FM broadcasting maps (BMs)." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Tianjin University, "The method is based on particle swarm optimization (PSO)-assisted support vect or machines (SVMs) and takes Beijing and its surrounding areas as an example for practice. FM BMs are constructed based on three typical propagation prediction models and measurements collected in Beijing and its surrounding areas. The meas urements encompass 11 frequencies ranging from 86.7 to 106.6 MHz, encompassing v arious reception scenarios such as rural and urban areas. The proposed method co nsiders factors such as the receiving location, terrain, frequency, and other re levant information specific to the Beijing area. It matches different areas with suitable propagation models to construct the FM BMs, combining the strengths of the three models. To verify the proposed method, we employ the relative error a s the evaluation criterion to evaluate the prediction performance of the propose d and the typical model. The comparison demonstrates the significant advantages of the proposed method in terms of prediction accuracy and stability. This resea rch is a valuable reference for regional application and localization research o f radio wave propagation prediction methods in the FM broadcasting band."
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    Southeast University Medical School Reports Findings in Bladder Cancer (Intracor poreal urinary diversion offers the advantage of delaying postoperative renal fu nction injury in patients undergoing robot-assisted radical cystectomy)

    91-92页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Bladder Can cer is the subject of a report. According to news originating from Nanjing, Peop le's Republic of China, by NewsRx correspondents, research stated, "To analyze c hanges in renal function and associated risk factors in patients with bladder ca ncer undergoing robot-assisted radical cystectomy (RARC) with intracorporeal or extracorporeal urinary diversion (ICUD or ECUD). Clinical-pathological data was extracted from electronic medical records of 266 patients with bladder cancer wh o underwent RARC at our institution between August 2015 and August 2022." Our news journalists obtained a quote from the research from Southeast Universit y Medical School, "Postoperative renal function was assessed using the estimated glomerular filtration rate (eGFR). Patients were classified into ECUD and ICUD groups based on the surgical approach. Significant differences in eGFR were obse rved between the two groups at 1, 2, and 3 years postoperatively. Moreover, 112 patients (42.1 %) experienced long-term renal function injury. Indep endent risk factors for long-term renal function injury included the type of sur gical approach, ureteroenteric anastomotic strictures, and pathological stage T3 or above. In terms of short-term renal function, 30 cases of acute kidney injur y (AKI) were observed, with an incidence rate of 11.3%. No differen ce in AKI incidence was found between the groups. Postoperative AKI and chronic kidney injury are prevalent complications following RC. This study highlights th at pathological stage, ureteroenteric anastomotic strictures, and ECUD significa ntly impact long-term renal function, but the type of urinary diversion (ileal c onduit or orthotopic neobladder) had no effect on renal function, and ICUD was s uperior in terms of long-term renal injury rate."
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    Patent Issued for Prediction of service completion time for vehicle service (USP TO 12087109)

    92-95页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Ani Technologies Private Limited (Beng aluru, India) has been issued patent number 12087109, according to news reportin g originating out of Alexandria, Virginia, by NewsRx editors. The patent's inventors are Agarwal, Gaurav (Bangalore, IN), Chakrapani, Hrishike sh Bharadwaj (Bangalore, IN), Chourasia, Smruti (Jabalpur, IN), Hinduja, Hitesh (Thane, IN), Mahipal, Punit (Bangalore, IN), Vsr, Krishna Koushik (Nellore, IN). This patent was filed on December 22, 2021 and was published online on September 10, 2024. From the background information supplied by the inventors, news correspondents o btained the following quote: "Transportation constitutes an important aspect of the modern world. For transport, individuals may utilize various types of vehicl es or automobiles such as motorbikes, autorickshaws, cars, buses, trucks, or the like. Modern vehicles (e.g. cars) are complex machines that include various veh icular systems such as air-conditioning (AC) systems, braking systems, suspensio n systems, or the like. These vehicles require periodic servicing to ensure sati sfactory operation of corresponding vehicular systems and safety of passengers t ravelling in these vehicles.
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    Patent Application Titled "Work Teaching Device and Work Teaching Method for Rob ot" Published Online (USPTO 20240300098)

    95-98页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originatin g from Washington, D.C., by NewsRx journalists, a patent application by the inve ntors INOUE, Tomohiro (Tokyo, JP); ISHIKAWA, Shinichi (Tokyo, JP); KOYAMA, Masah iro (Tokyo, JP); TAKAHASHI, Hiroki (Tokyo, JP), filed on March 15, 2021, was mad e available online on September 12, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: "A robot teaching system of PTL 1 is configured such that a te aching tool with a button for specifying a teaching position of a robot is adjus ted to a position where a teacher wants the robot to move, and by pressing the b utton here, the teaching position is specified, and further a position and postu re of the teaching tool here is measured with a stereo camera and the position i s determined as the teaching position of the robot, and then a teaching program is generated which interpolates between the teaching positions determined here a nd moves the robot.
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    Researchers Submit Patent Application, "Device Priority Prediction Using Machine Learning", for Approval (USPTO 20240303174)

    99-101页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-From Washington, D.C., NewsRx journali sts report that a patent application by the inventors Kote, Nithish (Bangalore, IN); Manvi, Sajit Siddalingappa (Bangalore, IN); Sethi, Parminder Singh (Ludhian a, IN), filed on March 7, 2023, was made available online on September 12, 2024. No assignee for this patent application has been made. News editors obtained the following quote from the background information suppli ed by the inventors: "Datacenters may include, for example, thousands of interco nnected computing devices capable of hosting a large number of applications. The computing devices may malfunction and reach critical states where the devices a re non-operational or close to failure. The critical states may be due to, for e xample, hardware errors and/or lack of available compute resources. Multiple com puting devices may be in a critical state at a given time, with each requiring a ttention and action to resolve the device issues. Given the large scale of moder n datacenter designs, there is a chAllenge in determining which computing device s should have priority when addressing device issues and failures."
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